Create app.py

app.py

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+from typing import Optional
+import chainlit as cl
+from langchain_chroma import Chroma
+from langchain_core.prompts import PromptTemplate
+from langchain_core.callbacks import CallbackManager, AsyncCallbackManagerForLLMRun
+from langchain_community.llms import LlamaCpp
+from chainlit.types import ThreadDict
+from langchain.chains import RetrievalQA, ConversationChain
+from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.chains.conversation.memory import ConversationBufferMemory
+
+
+
+# ctransformers is no longer used
+""" from langchain_community.llms import CTransformers
+
+# Initialize the language model
+llm = CTransformers(model='Model/llama-2-7b-chat.ggmlv3.q2_K.bin',  # 2 bit quantized model
+                    model_type='llama',
+                    config={'max_new_tokens': 256,  # max tokens in reply
+                            'temperature': 0.01, }  # randomness of the reply
+                    )
+ """
+
+# Initialize the language model with LlamaCpp
+llm = LlamaCpp(model_path="Model/llama-2-7b-chat.Q4_K_M.gguf",  #  token streaming to terminal
+               device="cpu",verbose = True, max_tokens = 4096,  #offloads ALL layers to GPU, uses around 6 GB of Vram
+               config={  # max tokens in reply
+                       'temperature': 0.75}  # randomness of the reply
+               )
+
+DATA_PATH = 'Data/'
+
+DB_CHROMA_PATH = 'vectorstore/db_chroma'
+
+embedding_function = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
+
+db = Chroma(persist_directory=DB_CHROMA_PATH, embedding_function=embedding_function)
+
+
+rag_pipeline = RetrievalQA.from_chain_type(
+    llm=llm, chain_type='stuff',
+    retriever=db.as_retriever(),
+    return_source_documents=True
+)
+
+
+template = """
+You are an AI specialized in the medical domain. 
+Your purpose is to provide accurate, clear, and helpful responses to medical-related inquiries. 
+You must avoid misinformation at all costs. Do not respond to questions outside of the medical domain. 
+If you are unsure or lack information about a query, you must clearly state that you do not know the answer.
+
+Question: {query}
+
+Answer:
+
+"""
+
+prompt_template = PromptTemplate(input_variables=["query"],template=template)
+
+
+
+conversation_buf = ConversationChain(
+    llm=llm,
+    memory=ConversationBufferMemory(), 
+)
+
+
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    pass
+
+
+
+
+@cl.step(type="llm")
+def get_response(query):
+    """
+    Generates a response from the language model based on the user's input. If the input includes
+    '-rag', it uses a retrieval-augmented generation pipeline, otherwise, it directly invokes
+    the language model.
+
+    Args:
+        question (str): The user's input text.
+
+    Returns:
+        str: The language model's response, potentially including source documents if '-rag' was used.
+    """
+    
+   
+    if "-rag" in query.lower():
+        response = rag_pipeline(prompt_template.format(query=query))
+        result = response["result"]
+        source = response["source_documents"]
+        if source:
+            source_details = "\n\nSources:"
+            for source in source:
+                page_content = source.page_content 
+                page_number = source.metadata['page']
+                source_book = source.metadata['source']
+                source_details += f"\n- Page {page_number} from {source_book}: \"{page_content}\""
+        
+            result += source_details
+        return result
+        
+     
+    return llm.invoke(prompt_template.format(query=query))
+
+
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    """
+    Fetches the response from the language model and shows it in the web ui.
+    """
+    try:
+        response = get_response(message.content)
+        msg = cl.Message(content=response)
+    except Exception as e:
+        msg = cl.Message(content=str(e))
+
+    await msg.send()
+
+
+
+
+@cl.on_chat_resume
+async def on_chat_resume(thread: ThreadDict):
+    pass # TODO user history gets fed to LLM 
+
+
+
+@cl.on_chat_end
+def on_chat_end():
+    pass 
+
+
+
+@cl.password_auth_callback
+def auth_callback(username: str, password: str):
+    # Fetch the user matching username from your database
+    # and compare the hashed password with the value stored in the database
+    if (username, password) == ("karcan", "karcan123"):
+        return cl.User(
+            identifier="admin", metadata={"role": "admin", "provider": "credentials"}
+        )
+    else:
+        return None